Non-invasive method and apparatus for determining onset of physiological conditions

ABSTRACT

The invention relates to the modelling and design of early warning systems for detecting medical conditions using physiological responses. The device comprises sensors for monitoring physiological parameters such as skin impedance, heart rate, and QT interval of a patient, means for establishing when those parameters change, the rate of change of the parameters, and a neural network processor for processing the information obtained by the sensors. The neural network processor is programmed with a fast learning algorithm. When the neural network establishes that a physiological condition is present in the patient an alarm signal will be generated. The invention extends to a method of non-invasive monitoring of a person using a neural network programmed with a fast learning algorithm. A non-invasive hypoglycaemia monitor is specifically described.

FIELD OF THE INVENTION

[0001] The present invention relates to the modelling and design ofearly warning systems using physiological responses. In particular suchsystems can be used for early detection of medical conditions, anon-invasive hypoglycaemia monitor for example. Although thisspecification concentrates on a system and method for the detection ofhypoglycaemia, it should be understood that the invention has widerapplication.

BACKGROUND OF THE INVENTION

[0002] Hypoglycaemia is the most common complication experienced bypatients with insulin dependent diabetes mellitus. Its onset ischaracterised by symptoms which include sweating, tremor, palpitations,loss of concentration and tiredness. Although the majority of patientscan use these symptoms to recognise the onset of hypoglycaemia and takecorrective action, a significant number of patients develophypoglycaemic unawareness and are unable to recognise the onset ofsymptoms.

[0003] Concerning hypoglycaemia, the blood glucose in men can drop to 3mmol/L after 24 hrs of fasting and to 2.7 mmol/L after 72 hrs offasting. In women, glucose can be low as 2 mmol/L after 24 hrs offasting. Blood glucose levels below 2.5 mmol/L are almost alwaysassociated with serious abnormality. Hypoglycaemia in diabetic patientshas the potential to become dangerous. In many cases of hypoglycaemia,the symptoms can occur without the awareness of the patient and at anytime, eg. while driving or even during deep sleep. In severe cases ofhypoglycaemia, the patient can lapse into a coma and die. Nocturnalepisodes are also potentially dangerous and have been implicated whendiabetic patients have been found unexpectedly dead in bed.Hypoglycaemia is one of the complications of diabetes most feared bypatients, on a par with blindness and renal failure.

[0004] Current technologies used for diabetes diagnostic testing andself-monitoring are known. For example, glucose meter manufacturers havemodified their instruments to use as little as 2 μl of blood and produceresults in under a minute. However, devices which require a blood sampleare unsatisfactory in that the sample is painful to obtain, andcontinuous monitoring is not possible.

[0005] There are only a few manufacturers who have produced non-invasiveblood glucose monitoring systems. The Diasensor 1000 from BiocontrolTechnology Inc. uses near-infrared technology and multivariateregression to estimate blood glucose levels. The system is veryexpensive, it has to be individually calibrated to each patient, it hasto be recalibrating every three months, and the calibration processtakes up to seven days. The GlucoWatch monitor from Cygnus is designedto measure glucose levels up to three times per hour for 12 hours. TheAutoSensor (the disposable component) which is attached to the back ofthe GlucoWatch monitor and adheres to the skin will provide 12 hours ofmeasurement. The product uses reverse iontophoresis to extract andmeasure glucose levels non-invasively using interstitial fluid. Itrequires 12 hours to calibrate, only provides 12 hours of measurement,requires costly disposable components, and the measurement has a timedelay of 15 minutes. Another device, the Sleep Sentry, monitorsperspiration and a drop in body temperature to alert the patient to theonset of hypoglycaemia. In studies of patients admitted for overnightmonitoring it was found to be unreliable in between 5% and 20% of cases.

[0006] During hypoglycaemia, the most profound physiological changes arecaused by activation of the sympathetic nervous system. Among thestrongest responses are sweating and increased cardiac output. Sweatingis centrally mediated through sympathetic cholinergic fibres, while thechange in cardiac output is due to an increase in heart rate andincrease in stroke volume.

[0007] It is an object of the invention to provide a non-invasive methodof detecting medical conditions in patients, which is relativelyaccurate and relatively inexpensive to use, and will trigger an alarmsignal within an acceptable time delay from when a condition which isbeing monitored for presents itself

SUMMARY OF THE INVENTION

[0008] According to a first aspect of the invention there is provided anon-invasive method of determining the presence or onset of aphysiological condition in a person comprising the steps of:

[0009] continuously monitoring two or more of at least the followingparameters of the patient: skin impedance, heart rate, QT interval, andmean or peak frequency of the alpha wave;

[0010] establishing whether one or more of those monitored parameterschanges, and if so, the rate of change of that parameter or parameters;

[0011] feeding data obtained in the first two steps into a neuralnetwork processor programmed with a fast learning algorithm; and

[0012] causing an alarm signal to be triggered when said neural networkestablishes conditions which suggest the presence or imminent onset ofsaid physiological condition.

[0013] The monitoring of the heart rate and QT interval is preferablydone with a ECG. The monitoring of the alpha wave is preferably donewith an EEG. The fast learning algorithm may have either a magnifiedgradient function, or an optimal gradient function, or may be a robustsliding mode learning algorithm.

[0014] The invention extends to apparatus for generating an alarm when aphysiological condition is present or imminent in a person, saidapparatus comprising:

[0015] sensors for sensing at least two of the skin impedance, heartrate, QT interval, and mean or peak frequency of the alpha wave;

[0016] means for establishing when one or more of the sensed parameterschanges and when so established, the rate of change of said changedparameters,

[0017] a neural network linked to said sensors and said means so as toreceive a substantially continuous data stream from said sensors andsaid means, the neural network being programmed with a fast learningalgorithm and adapted to establish when the sensed parameters, and anychange to those parameters, for a particular person are such as toindicate the presence or imminent onset of said physiological condition,

[0018] and alarm means linked to said neural network adapted to betriggered when the presence or imminent onset of said physiologicalcondition is established.

[0019] The apparatus may include a magnified gradient function, anoptimal gradient function, or is a robust sliding mode learningalgorithm.

BRIEF DESCRIPTION OF THE DRAWINGS

[0020]FIG. 1 shows the basic structure of a two-layer feed forwardneural network.

[0021]FIG. 2 shows blood glucose level estimations using multipleregression (left) and neural network (right) for three diabeticpatients.

DETAILED DESCRIPTION OF THE INVENTION

[0022] In the development of the device, analysis of the effectivenessof skin impedance, ECG (in particular heart rate and QT interval) andEEG by means of a robust neural networks provides a novel basis forearly detection of a medical condition such as hypoglycaemia as well asan indirect measurement of blood glucose levels. There are numerousfactors which can affect the accuracy with which a medical condition isprecluded such as environment conditions, stress, and the like. Thedevice should be capable of differentiating between effects caused byenvironmental conditions and those which indicate the presence of oronset of a particular medical condition.

[0023] The possibility of hypoglycaemia induced arrythmias, andexperimental hypoglycaemia has been shown to prolong QT intervals anddispersion in both non-diabetic subjects and in those with Type 1 andType 2 diabetes. Another important physiological change is that aslowing of the a rhythm in EEG (8-13 Hz) in response to hypoglycaemiaappears at blood glucose values of approximately 2.5 mmol/L and is theearliest abnormality.

[0024] In broad terms, a device which is capable of initiating andcorrectly interpreting a wide range of physiological signals could beused for the detection of conditions such as hypoglycaemia,hyperglycaemia, or may be used to provide indirect measurement of bloodglucose levels. It may also be used for the detection of sudden infantdeath syndrome, chronic stress, sleep disorders and driver fatigue forexample. Indeed, other medical conditions which present themselves by arange of different physiological indications could be detected using themethod and apparatus of the invention. Because physiological signalsdiffer from patient to patient it is important that a device is able to“learn” when a particular set of signals represent the onset or presenceof a medical condition in a particular patient, and disregard falsesignals which might be caused by environmental or other factors.

[0025] There are many different ways to implement the signal sensing andsignal conditioning for the device. One implementation strategy can bedescribed as follows.

[0026] Skin moisture (sweating) can be measured using skin impedancemonitoring. A concentric type electrode may be used which contains anouter passive electrode (10 mm inner and 20 mm outer diameter) and aninner electrode (5 mm diameter). A sinusoidal constant current source of100 kHz 10 μA may be applied by a Wien bridge oscillator to the innerelectrodes, and a voltage produced in accordance to the skin impedance,at the outer electrode. The signal from the outer electrode may beamplified by an instrumentation amplifier, passed through a Butterworthlow-pass filter (cut-off freq=140 kHz) and fed through an AC-DCconverter to produce a DC signal proportional to the skin impedance.

[0027] The ECG may be achieved by placing three Ag—AgCl electrodes in aLeadII configuration on the patient's chest. The signal obtained fromthe electrodes may then be amplified using an instrumentation amplifierwith gain of 10 and CMRR>100 dB at 100 Hz. This feeds through ahigh-pass filter with cutoff frequency of 0.5 Hz. A second stagenon-inverting amplifier may be added to provide a gain of 100. To obtaina reliable heart rate of the patient, a bandpass filter may be used, todetect the QRS complex of the ECG signal. A threshold circuit togetherwith a comparator may be used to reliably detect the R slope. The QTinterval, on the other hand is a clinical parameter which can be derivedfrom the ECG signal. During hypoglycaemia, the QT interval increases. QTmeasurement requires the identification of the start of QRS complex andthe end of the T wave. The intersection of the isoelectric line and atangent to the T wave can be used to measure the QT interval.

[0028] EEG signals may be obtained using a pair of Ag-AgCl electrodes onO₁ and O₂ sites on the posterior cortex. The conditioning circuitryincludes a two op-amp instrumentation amplifier to obtain high overallgain. Low voltage and current noise CMOS amplifiers may be used for EEGrecordings to reproduce these signals for diagnostic purposes. In thisinstrumentation amplifier configuration, an integrator in the feedbackloop provides a low overall gain for the low-frequency input signals.For high resolution, the digital sampling rate per channel may be 256 Hzand data may be stored in one-second epochs. Signals may be analysedusing Fast Fourier Transform (FFT). The mean frequency or the peakfrequency of the α wave in EEG can then be derived.

[0029] The monitoring for hypoglycaemia and blood glucose level isdifficult because of imperfections caused by possible conflicting orreinforcing responses from skin impedance, ECG and EEG. This conflictinginformation is handled in the framework of a robust neural network inorder to obtain accurate determinations from a complex uncertainnon-linear physiological system.

[0030] For hypoglycaemia detection using a combination of four variables(skin impedance, heart rate, QT interval and mean or peak frequency ofthe α wave) the analysis is akin to a black box belonging to a givenclass of nonlinear systems. A neuro-estimator is suitable for complexestimates. A neuro-estimator may be embedded in an EEPROM of the systemto monitor hypoglycaemia episodes in patients. This neural network has amultilayer feedforward neural network structure with one input layer,one hidden layer and one output layer as shown in FIG. 1. Essentially,this neural network is trained using a learning algorithm in whichsynaptic strengths are systematically modified so that the response ofthe network will increasingly approximate the blood glucose status givenby the available qualitative data.

[0031] The back-propagation (BP) algorithm is a widely appliedmultilayer neural-network learning algorithm. Unfortunately, it suffersfrom a number of shortcomings. One such shortcoming is its slowconvergence. A preferred system will implement real-time learning so asto be able to adapt to the physiological signals of individual patients.

[0032] The learning algorithms for updating the weight matrices may bebased on a magnified gradient algorithm or a sliding mode strategy. Thegradient descent back-propagation (BP) learning algorithm for updatingthe weight matrices, the error signal terms for output layer and hiddenlayer respectively can be found from: $\begin{matrix}{\delta_{k} = {{- \frac{\partial E}{\partial v_{k}}} = {\left( {R_{k} - z_{k}} \right)\frac{\partial z_{k}}{\partial v_{k}}}}} & {{\overset{\_}{\delta}}_{j} = {{- \frac{\partial E}{\partial{\overset{\_}{v}}_{j}}}{\sum\limits_{k = 1}^{K}{\delta_{k}w_{kj}}}}} \\{W^{*} = {{W - {\eta \frac{\partial E}{\partial W}}} = {W + {{\eta\delta}\quad y^{\prime}}}}} & {{\overset{\_}{W}}^{*} = {{W - {\eta \frac{\partial E}{\partial W}}} = {\overset{\_}{W} + {\eta \overset{\_}{\delta}\quad x^{\prime}}}}}\end{matrix}$

[0033] For faster network convergence suitable for real-time learning, amagnified gradient function (MGF) in adaptive learning can be used,where the error signal terms for output layer and hidden layer can bemagnified with a constant S (usually between 1 and 5): $\begin{matrix}{\left. {\delta_{j} = {{- \frac{\partial E}{\partial{\overset{\_}{v}}_{k}}} = \left( {R_{k} - z_{k}} \right)}} \right)\left( \frac{\partial z_{k}}{\partial v_{kj}} \right)^{\frac{1}{S}}} & {{\overset{\_}{\delta}}_{j} = {{- \frac{\partial E}{\partial{\overset{\_}{v}}_{j}}} = {\left( \frac{\partial y_{j}}{\partial{\overset{\_}{v}}_{j}} \right)^{\frac{1}{S}}{\sum\limits_{k = 1}^{K}{\delta_{k}w_{kj}}}}}} \\{{{\left( \frac{\partial E}{\partial t} \right)_{MGF}} - {\left( \frac{\partial E}{\partial t} \right)_{BP}}} > 0} & \quad\end{matrix}$

[0034] MGF-PROP retains the gradient-descent property and theconvergence rate of MGF-PROP is faster than that of BP. This algorithmcan be implemented in real-time relatively easily.

[0035] Similar to the above solution, it is also possible to develop aback propagation algorithm based on sliding mode for updating the weightmatrices. This type of algorithm should be faster as the rate ofconvergence can be controlled, and is more robust against parameteruncertainty and strong disturbances, as the error will be forced toslide along a pre-determined hyperplane.

[0036] In order to detect hypoglycaemia episodes reliably, it is not asimple matter of just using a combination of the above-mentionedparameters: skin impedance, heart rate, QT interval, mean or peakfrequency of the α wave. The main difficulty is different patients havedifferent base values of these parameters. In addition, these basevalues may vary from day to day.

[0037] False detection may arise from other environmental or personalconditions which could cause similar variations in sweating and heartrate such as the occurrence of nightmares, sudden change in weather,etc. Avoidance of false detection is important if the system is to berelied on by sufferers of acute or life threatening conditions.

[0038] As a consequence, the main parameters used for the detection ofhypoglycaemia are not only skin impedance, heart rate, QT interval ormean/peak frequency of the α wave, but also their rates of change. Theadditional parameters are the rates of change in skin impedance, heartrate, QT interval and mean/peak frequency of the α wave. Other importantparameters are the time constants associated with these physiologicalresponses. Rates of changes and the time constants inherent inphysiological responses are important factors which can be used toreject or minimise false detection.

[0039] It is possible to model the dynamic neural network which is usedto estimate blood glucose levels as:$\frac{x}{t} = {{f(x)} + {{g(x)} \cdot u}}$

[0040] where x is the state of the neural network and σ and φ aresigmoidal vector functions. Note that x contains the skin impedance,heart rate, QT interval, peak frequency of the α wave, and their ratesof changes. The nonlinear functions ƒ(x) and g(x) contain the timecontants associated with the state vector x. In other words, thisequation describes how fast the important physiological parameters suchas the skin impedance, heart rate, QT interval, and peak a frequencyrespond to a reduction of blood glucose levels.

[0041] The above model also allows the identification of modelvariations and disturbances to ensure that the convergence of theleaning algorithm is assured. This is important for providing real-timeneural network adaptation to a specific patient for the detection of aphysiological condition such as hypoglycaemia under various conditions.

[0042] Using the above important main parameters for hypoglycaemiadetection, the learning algorithms for updating the weight matricesbased on a magnified gradient algorithm or a sliding mode strategyallows the neural network to adapt on-line to a particular patient veryeffectively or to provide robust estimation in the presence ofdisturbances (initial state, system and observation noises) to minimisefalse detection.

[0043] A combination or all of these parameters are fed into a genericneural network for the detection of hypoglycaemia or the estimation ofblood glucose levels. FIG. 2 shows the estimation of blood glucoselevels using only skin impedance and heart rate for three diabeticpatients. In FIG. 2, the result of a multiple regression technique usedto evaluate corresponding blood glucose levels is shown on the left withgood correlation (R²=0.792), and the result of a trained neural networkis shown on the right with a very strong correlation (R²=0.977).

[0044] It is envisaged that the device, once properly trained, should becapable of not only determining the onset or presence of a condition,but also bale to assign a value to that condition. Thus, for example, ifthe device is able to accurately estimate actual blood glucose levels,then the patient should be able to use that estimation to modify quantumand timing of medication.

[0045] In practice, a trained neural network would be obtained off-linefor many patients, but the described neural network should have theability to adapt to a particular patient. This hypoglycaemia monitor canquickly fine tune the neural network for better estimation of bloodglucose levels or hypoglycaemia conditions, using either the magnifiedgradient function back propagation technique (MGF-PROP) or the slidingmode back propagation technique (SM-PROP). Both of these two techniquescan be implemented in real-time with very fast convergence.

[0046] It is envisaged that communication between the sensors and theprocessor may be via a telemetric system. Radio frequency transmittersand receivers or transceivers (typically 433 MHz or 2.4GHz) may be used.

[0047] The alarm may be of any convenient type, and might comprise asimple radio alarm, a signal transmitted to a monitoring station, or thelike.

[0048] It is also preferred that data transmitted from the sensors willbe continuously logged. The system may be interfaced with a PC whichwill continuously log the relevant data using a data management systemsuch as Labview.

[0049] Clearly the invention can vary from that described herein withoutdeparting from the scope of the invention. In particular the fastlearning algorithm need not be of the type described herein, but anyfast learning algorithm that is able to provide substantially real timeanalysis of multiple data streams in the manner described herein couldbe used.

1. A non-invasive method of determining the value, presence or onset ofa physiological condition in a person comprising the steps of:continuously monitoring two or more of at least the following parametersof the patient: skin impedance, heart rate, QT interval, and mean orpeak frequency of the alpha wave; establishing whether one or more ofthose monitored parameters changes, and if so, the rate of change ofthat parameter or parameters; feeding data obtained in the first twosteps into a neural network processor programmed with a fast learningalgorithm; and causing a signal to be generated when said neural networkestablishes conditions which suggest the presence or imminent onset ofsaid physiological condition, or has estimated the value of saidphysiological condition.
 2. A non-invasive method according to claim 1wherein the monitoring of the heart rate and QT interval is done with anECG.
 3. A non-invasive method according to either preceding claimswherein the monitoring of the alpha wave is done with an EEG.
 4. Anon-invasive method according to any preceding claim wherein the firstlearning algorithm has either a magnified gradient function or anoptimal gradient function.
 5. A non-invasive method according to any oneof claims 1 to 3 wherein the fast learning algorithm is a robust slidingmode algorithm.
 6. A non-invasive method according to any precedingclaim wherein the physiological condition determined is any one ofhypoglycaemia, hyperglycaemia, sudden infant death syndrome, chronicstress, sleep disorders, and driver fatigue
 7. Apparatus for generatingan alarm when a physiological condition is present or imminent in aperson, said apparatus comprising: sensors for sensing at least two ofthe skin impedance, heart rate, QT interval, and mean or peak frequencyof the alpha wave; means for establishing when one or more of the sensedparameters changes and when so established, the rate of change of saidchanged parameters, a neural network linked to said sensors and saidmeans so as to receive a substantially continuous data stream from saidsensors and said means, the neural network being programed with a fastlearning algorithm and adapted to establish when the sensed parameters,and any change to those parameters, for a particular person are such asto indicate the presence or imminent onset of said physiologicalcondition, and alarm means linked to said neural network adapted to betriggered when the presence or imminent onset of said physiologicalcondition is established.
 8. Apparatus according to claim 7 wherein thefast learning algorithm has either a magnified gradient function or anoptimal gradient function.
 9. Apparatus according to claim 7 wherein thefast learning algorithm is a robust sliding mode algorithm. 10.Apparatus according to any one of claims 7 to 9 wherein an ECG is usedto obtain the data for the hear rate and QT interval.
 11. Apparatusaccording to any one of claims 7 to 10 wherein an EEG is used to obtainthe data for the mean or peak alpha wave.
 12. Apparatus according to anyone of claims 7 to 11 wherein data is transmitted between the sensorsand the neural network by radio frequency.
 13. Apparatus according toany one of claims 7 to 12 wherein the device is adapted to estimateactual value of a physiological condition of a patient.
 14. Apparatusaccording to claim 13 wherein the physiological condition which thedevice is adapted to estimate is the blood glucose level of the patient.15. A method substantially as hereinbefore described with reference tothe accompanying drawings.